Automated control of cell culture using Raman spectroscopy

ABSTRACT

The monitoring and control of bioprocesses is provided. The present disclosure provides the ability to generate generic calibration models, independent of cell line, using inline Raman probes to monitor changes in glucose, lactate, glutamate, ammonium, viable cell concentration (VCC), total cell concentration (TCC) and product concentration. Calibration models were developed from cell culture using two different CHOK1SV GS-KO™ cell lines producing different monoclonal antibodies (mAbs). Developed predictive models, qualified using an independent CHOK1SV GS-KO™ cell line not used in calibration, measured changes in glucose, lactate, ammonium, VCC, and TCC with minor prediction errors over the course of cell culture with minimal cell line dependence. The development of these generic models allows the application of spectroscopic PAT techniques in a clinical manufacturing environment, where processes are typically run once or twice in GMP manufacturing based on a common platform process.

RELATED APPLICATIONS

The present application is based on and claims priority to U.S.Provisional Patent Application Ser. No. 62/569,076 having a filing dateof Oct. 6, 2017, and U.S. Provisional Patent Application Ser. No.62/569,190 having a filing date of Oct. 6, 2017, both of which areincorporated herein by reference in its entirety.

BACKGROUND

Bioreactors, which are apparatuses in which biological reactions orprocesses can be carried out on a laboratory or industrial scale, areused widely within the biopharmaceutical industry. Bioreactors can beused to produce all different types of bioproducts. Bioproducts caninclude, for instance, cell cultures and materials derived from cellcultures including beverages, biofuels, bioenergy, biochemicals,antibiotics, amino acids, enzymes, monoclonal antibodies, monomers,proteins, food cultures, biopolymers, alcohols, flavorings, fragrances,and the like. In some embodiments, cell cultures can be grown for celltherapy. Cell therapy is the prevention, treatment, cure or mitigationof disease or injuries in humans by the administration of autologous,allogeneic or xenogeneic cells that have been manipulated or altered exvivo. One goal of cell therapy is to repair, replace or restore damagedtissues or organs.

Cell cultures are typically grown in batch processes where thebiological material remains in the bioreactor until the end of thereaction time. In certain of these processes, fluid medium containedwithin the bioreactor can be periodically or continuously removed andresupplied in order to replenish nutrients contained within the fluidmedium and for possibly removing damaging by-products that are producedduring the process.

During the growth of cell cultures, the regulation of key nutrients inthe medium can have a direct impact on the quality of the product thatis produced. For example, various carbohydrates, such as glucose, arefed to bioreactors in order to promote cell growth. Less than optimumglucose levels, however, can stunt or inhibit growth. For instance,lower glucose levels can starve cell cultures and lead to the build-upof waste. Simply increasing glucose levels to prevent depletion can alsolead to a dramatic fluctuations in glucose levels which also adverselyaffect cell growth. Attempting to maintain optimum nutrient and wastelevels in cell cultures can be unpredictable and subject tounforeseeable changes when the cell culture is not constantly monitored.

Historically, upstream bioprocesses have been monitored by removingsamples that are then analyzed for selected metabolites, cell growth,and product concentration using offline methods, with continuous realtime measurements being limited in scope (e.g., pH, dissolved oxygentension (DOT), temperature). The offline methods require trainedoperators, are often labor intensive, and generate waste through the useof expensive reagents and samples. Additionally, offline measurementtypically occurs at infrequent intervals (e.g., every 12 or 24 hrs)which can potentially miss shifts in cell metabolism that may beindicative of abnormal processes. Furthermore, every sample removed fromthe bioreactor carries the added potential risk for contamination.

Recent process improvement efforts within the industry have focused onidentifying process analytical technologies that can be used tocontinuously monitor and control bioprocesses in real time. While theseoptions enable more frequent monitoring of the bioreactor process, theyrequire continuous sample removal from the bioreactor and still requirethe use of expensive reagents for analysis. Additionally, concerns overthe scalability of these systems and the increased potential risk ofcontamination from sample removal make these options less desirable forcontinuous process monitoring. In view of the above, a need exists for aprocess and system for monitoring biochemical and biopharmaceuticalprocesses such as processes for propagating cell cultures that isnoninvasive and allows for continuous or periodic adjustments in orderto maintain optimum conditions within a bioreactor.

SUMMARY

The present disclosure is generally directed to a processing system forpropagating biomaterials, such as cell cultures. In one embodiment, forinstance, the processing system of the present disclosure is directed topropagating mammalian cell cultures. In an alternative embodiment, thesystem and process of the present disclosure can be used for propagatingcells for cell therapy. Cell cultures processed in accordance with thepresent disclosure, for instance, can include stem cells, T cells, andimmune cells, including B cells, natural killer cells, dendritic cells,tumor infiltrating lymphocytes, monocytes, megakaryocytes, and the like.In accordance with the present disclosure, Raman spectroscopy is used inorder to monitor one or more parameters of a bioprocess within abioreactor. The use of Raman spectroscopy in accordance with the presentdisclosure allows for the periodic or continuous monitoring of one ormore parameters in a bioprocess without the disadvantages associatedwith offline sampling. For example, Raman spectroscopy can reduce thevolume and analysis time required for parameter concentration analysis.In accordance with the present disclosure, Raman spectroscopy is coupledwith a control system which allows for the automation of process speedswhich results in improved process robustness and control. In oneembodiment, for instance, the control system can include a predictivemodel that extrapolates parameter concentrations in the future formaintaining the bioprocess environment within carefully controlledlimits.

In one embodiment, for instance, the present disclosure is directed to aprocess for propagating a cell culture. The process includes exposing acell culture and a bioreactor to a coherent light source causing lightto scatter. The coherent light source, for instance, may comprise alight beam emitted by a laser. The light contacting the cell culture, inone embodiment, can have a wavelength of from about 400 nm to about 1500nm, such as from about 700 nm to about 850 nm.

An intensity of the scattered light is measured using Ramanspectroscopy. A concentration of at least one parameter in the cellculture is determined based upon the measured intensity of light. In oneembodiment, for instance, the concentration is determined by acontroller. Based on the determined concentration of the parameter, thecontroller can then selectively increase or decrease flow of a parameterinfluencing substance to the bioreactor in order to maintain theparameter within preset limits.

The parameter measured according to the process can comprise, forinstance, glucose concentration, lactate concentration, glutamateconcentration, ammonium concentration, viable cell concentration, totalcell concentration, product concentration, or mixtures thereof. In oneembodiment, for instance, at least two, such as at least three, such asleast four different parameters are measured from the intensity of thescattered light using Raman spectroscopy. The controller can beconfigured to receive all of the concentration data and control one ormore parameter influencing substances. The parameter influencingsubstance, for instance, may comprise one or more nutrient medias. Forinstance, the controller may increase flow of a carbohydrate, such asglucose into the bioreactor. Alternatively, the controller can increasethe withdrawal of a fluid medium from the bioreactor in a process thatuses perfusion.

The concentration of the at least one parameter can be determined usingvarious methods. In one embodiment, for instance, the concentration ofthe parameter is determined by comparing the light intensity data toreference data contained within the controller. In one embodiment, thecontroller can include a predictive model that extrapolates a futureconcentration of the parameter based on the determined concentration ofthe parameter and can selectively increase or decrease at least oneparameter influencing substance in order to maintain the parameterwithin preset limits based on the calculated future concentration.

In determining the concentration of the parameter, statistical analysiscan be conducted on the scattered light intensity measured using Ramanspectroscopy. In one embodiment, for instance, a standard normal variatecan be applied to the measured scattered light intensity. After thestandard normal variate is applied, a first derivative can be appliedfollowed by detrending. The conducted statistical analysis can bemodeled using a least squares regression method. After preprocessing themeasured scattered light intensity, a spectral range can be selectedthat correlates to the parameter being monitored. The statisticalanalysis or the preprocessing of the measured scattered light intensitycan be conducted by the controller.

In one embodiment, the cell culture is propagated in a batch process forfrom about 2 days to about 28 days and then harvested. The concentrationof the at least one parameter can be determined within the first 12hours to 4 days of the process. The initial concentration data can thenbe used by the controller to selectively increase or decrease the flowrate of a parameter influencing substance after the initialmeasurements.

During the process, the concentration of the at least one parameter canbe determined periodically or continuously. In one embodiment, forinstance, concentration determinations can be made at least every 24hours, such as at least every 16 hours, such as at least every 8 hours,such as at least every 4 hours, such as at least every 2 hours, such asat least every hour.

The present disclosure is also directed to a system for propagating acell culture. The system includes a bioreactor defining a hollowinterior for receiving a cell culture. The bioreactor includes aplurality of ports for feeding and/or removing materials from the hollowinterior. A nutrient media feed for feeding a nutrient media to thehollow interior of the bioreactor is included in the system and is influid communication with at least one of the ports on the bioreactor.The system further includes a light conveying device in communicationwith the hollow interior of the bioreactor. The light conveying deviceis for conveying light to the bioreactor and for conveying light awayfrom the bioreactor.

The system further includes a coherent light source in communicationwith the light conveying device. The coherent light source exposes acell culture in the bioreactor to a beam of light. The coherent lightsource, for instance, may comprise a laser that is configured to emitlight at a wavelength of from about 400 nm to about 1500 nm.

The light conveying device is in communication with a Ramanspectrometer. The Raman spectrometer is for receiving scattered lightfrom the bioreactor after a cell culture has been exposed to a beam oflight from the coherent light source. The Raman spectrometer is formeasuring an intensity of the scattered light for determining theconcentration of one or more parameters contained within the bioreactor.

In accordance with the present disclosure, the system further includes acontroller. The controller can be in communication with the Ramanspectrometer for receiving information regarding the intensity of thescattered light. The controller can be configured to determine theconcentration of one or more parameters from the Raman spectrometer. Thecontroller can be further configured to control the nutrient media feedbased upon the determined parameter concentrations. The controller, forinstance, can selectively increase or decrease flow of a nutrient mediafrom the nutrient media feed to the bioreactor in order to maintain theconcentration of one or more parameters within preset limits.

The controller can comprise one or more microprocessors. As used herein,a microprocessor can include any programmable logic unit.

In one embodiment, the controller can be programmed with a predictivemodel that can extrapolate and predict the concentration of one or moreparameters in the future while the cell culture is propagating. Thecontroller can be configured to selectively increase or decreasenutrient feed to the bioreactor based on the determined concentration ofthe one or more parameters and based upon the future predictedconcentration of the one or more parameters.

Other features and aspects of the present disclosure are discussed ingreater detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

A full and enabling disclosure of the present disclosure is set forthmore particularly in the remainder of the specification, includingreference to the accompanying figures, in which:

FIG. 1 is a cross sectional view of one embodiment of a bioreactorsystem in accordance with the present disclosure;

FIG. 2A is a graphical representation of some of the results obtained inExample No. 1;

FIG. 2B is a graphical representation of some of the results obtained inExample No. 1;

FIG. 3A is a graphical representation of some of the results obtained inExample No. 1;

FIG. 3B is a graphical representation of some of the results obtained inExample No. 1;

FIG. 3C is a graphical representation of some of the results obtained inExample No. 1;

FIG. 3D is a graphical representation of some of the results obtained inExample No. 1;

FIG. 3E is a graphical representation of some of the results obtained inExample No. 1;

FIG. 3F is a graphical representation of some of the results obtained inExample No. 1;

FIG. 3G is a graphical representation of some of the results obtained inExample No. 1;

FIG. 4A is a graphical representation of some of the results obtained inExample No. 1;

FIG. 4B is a graphical representation of some of the results obtained inExample No. 1;

FIG. 4C is a graphical representation of some of the results obtained inExample No. 1;

FIG. 4D is a graphical representation of some of the results obtained inExample No. 1;

FIG. 4E is a graphical representation of some of the results obtained inExample No. 1;

FIG. 4F is a graphical representation of some of the results obtained inExample No. 1;

FIG. 4G is a graphical representation of some of the results obtained inExample No. 1;

FIG. 5 is a graphical representation of some of the results obtained inExample No. 1;

FIG. 6 is a graphical representation of some of the results obtained inExample No. 1;

FIG. 7 is a graphical representation of some of the results obtained inExample No. 2;

FIG. 8 is a graphical representation of some of the results obtained inExample No. 2;

FIG. 9 is a graphical representation of some of the results obtained inExample No. 2;

FIG. 10 is a graphical representation of some of the results obtained inExample No. 2;

FIG. 11 is a graphical representation of some of the results obtained inExample No. 2;

FIG. 12 is a graphical representation of some of the results obtained inExample No. 2;

FIG. 13 is a graphical representation of some of the results obtained inExample No. 2;

FIG. 14 is a graphical representation of some of the results obtained inExample No. 2;

FIG. 15 is a graphical representation of some of the results obtained inExample No. 2;

FIG. 16 is a graphical representation of some of the results obtained inExample No. 2;

FIG. 17 is a graphical representation of some of the results obtained inExample No. 2;

FIG. 18 is a graphical representation of some of the results obtained inExample No. 2;

FIG. 19 is a graphical representation of some of the results obtained inExample No. 2; and

FIG. 20 is a graphical representation of some of the results obtained inExample No. 2.

Repeat use of reference characters in the present specification anddrawings is intended to represent the same or analogous features orelements of the present invention.

DETAILED DESCRIPTION

It is to be understood by one of ordinary skill in the art that thepresent discussion is a description of exemplary embodiments only, andis not intended as limiting the broader aspects of the presentdisclosure.

In general, the present disclosure is directed to a process and systemfor producing a bioproduct. In one embodiment, for instance, the presentdisclosure is directed to a process and system for propagating a cellculture within a bioreactor. The system of the present disclosure canuse open loop or closed loop control for monitoring one or moreparameters in the bioreactor and then automatically changing or varyingthe flow of a parameter influencing substance into or out of thebioreactor. The autonomous control is coupled with Raman spectroscopywhich allows for continuous or periodic noninvasive monitoring of one ormore parameters within the bioreactor. Raman spectroscopy can, forinstance, continuously monitor and collect information within awavelength region, such as from about 800 nm to about 2500 nm, andcollect information about the overtones of fundamental absorption bandsobserved, which can be used to determine parameter concentrations.

Raman spectroscopy measures changes in the vibrational frequency ofcomponent specific molecular bonds. Raman provides complimentaryinformation to more traditional mid-IR spectroscopy, while having moreutility in aqueous solutions due to its resistance to waterinterference, making it desirable for bioreactor applications.

Raman spectra collected from inline Raman probes, coupled withmultivariate analysis (MVA), can monitor metabolites and cellconcentration. Raman spectroscopy provides the ability to monitorbioprocesses in real time which allows for the implementation offeedback controls for nutrient feeds leading to improved product qualityand cell productivity.

In accordance with the present disclosure, in line spectroscopy can becoupled with predictive model control. Of particular advantage, theprocess and system of the present disclosure can be scaled to variousdifferent bioreactor sizes and to various cell lines. For instance, thepredictive models used in accordance with the present disclosure arerobust and developed for platform processes that are not cell linedependent and thus can be used in clinical as well as commercialmanufacturing. In particular, it was discovered that certain parameterscontained in the bioreactor during the production of bioproducts aregeneric and thus not dependent upon specific cell line applications.

Referring to FIG. 1 , one embodiment of a bioreactor system inaccordance with the present disclosure is shown. The bioreactor systemincludes a bioreactor 10. In general, the system and process of thepresent disclosure can use any suitable bioreactor. The bioreactor, forinstance, may comprise a fermenter, a stirred-tank reactor, an adherentbioreactor, a wave-type bioreactor, a disposable bioreactor, and thelike. In the embodiment illustrated in FIG. 1 , the bioreactor 10comprises a hollow vessel or container that includes a bioreactor volume12 for receiving a cell culture within a fluid growth medium. As shownin FIG. 1 , the bioreactor system can further include a rotatable shaft14 coupled to an agitator such as dual impellers 16 and 18.

The bioreactor 10 can be made from various different materials. In oneembodiment, for instance, the bioreactor 10 can be made from metal, suchas stainless steel. Metal bioreactors are typically designed to bereused.

Alternatively, the bioreactor 10 may comprise a single use bioreactormade from a rigid polymer or a flexible polymer film. When made from arigid polymer, for instance, the bioreactor walls can be free standing.Alternatively, the bioreactor can be made from a flexible polymer filmor shape conforming material that can be liquid impermeable and can havean interior hydrophilic surface. In one embodiment, the bioreactor 10can be made from a flexible polymer film that is designed to be insertedinto a rigid structure, such as a metal container for assuming a desiredshape. Polymers that may be used to make the rigid vessel or flexiblepolymer film include polyolefin polymers, such as polypropylene andpolyethylene. Alternatively, the polymer can be a polyamide. In stillanother embodiment, a flexible polymer film can be formed from multiplelayers of different polymer materials. In one embodiment, the flexiblepolymer film can be gamma irradiated.

The bioreactor 10 can have any suitable volume. For instance, the volumeof the bioreactor 10 can be from 0.1 mL to about 25,000 L or larger. Forexample, the volume 12 of the bioreactor 10 can be greater than about0.5 L, such as greater than about 1 L, such as greater than about 2 L,such as greater than about 3 L, such as greater than about 4 L, such asgreater than about 5 L, such as greater than about 6 L, such as greaterthan about 7 L, such as greater than about 8 L, such as greater thanabout 10 L, such as greater than about 12 L, such as greater than about15 L, such as greater than about 20 L, such as greater than about 25 L,such as greater than about 30 L, such as greater than about 35 L, suchas greater than about 40 L, such as greater than about 45 L. The volumeof the bioreactor 10 is generally less than about 25,000 L, such as lessthan about 15,000 L, such as less than about 10,000 L, such as less thanabout 5,000 L, such as less than about 1,000 L, such as less than about800 L, such as less than about 600 L, such as less than about 400 L,such as less than about 200 L, such as less than about 100 L, such asless than about 50 L, such as less than about 40 L, such as less thanabout 30 L, such as less than about 20 L, such as less than about 10 L.In one embodiment, for instance, the volume of the bioreactor can befrom about 1 L to about 5 L. In an alternative embodiment, the volume ofthe bioreactor can be from about 25 L to about 75 L. In still anotherembodiment, the volume of the bioreactor can be from about 1,000 L toabout 5,000 L.

In addition to the impellers 16 and 18, the bioreactor 10 can includevarious additional equipment, such as baffles, spargers, gas supplies,heat exchangers or thermal circulator ports, and the like which allowfor the cultivation and propagation of biological cells. For example, inthe embodiment illustrated in FIG. 1 , the bioreactor 10 includes asparger 20 and a baffle 22. In addition, the bioreactor system caninclude various probes for measuring and monitoring pressure, foam, pH,dissolved oxygen, dissolved carbon dioxide, and the like.

As shown in FIG. 1 , the bioreactor 10 can include a rotatable shaft 14attached to impellers 16 and 18. The rotatable shaft 14 can be coupledto a motor 24 for rotating the shaft 14 and the impellers 16 and 18. Theimpellers 16 and 18 can be made from any suitable material, such as ametal or a biocompatible polymer. Examples of impellers suitable for usein the bioreactor system include hydrofoil impellers, high-soliditypitch-blade impellers, high-solidity hydrofoil impellers, Rushtonimpellers, pitched-blade impellers, gentle marine-blade impellers, andthe like. When containing two or more impellers, the impellers can bespaced apart along the rotating shaft 14.

As shown in FIG. 1 , the bioreactor 10 also includes a plurality ofports. The ports can allow supply lines and feed lines into and out ofthe bioreactor 10 for adding and removing fluids and other materials. Inaddition, the one or more ports may be for connecting to one or moreprobes for monitoring conditions within the bioreactor 10. In addition,the bioreactor 10 and be placed in association with a load cell formeasuring the mass of the culture within the bioreactor.

In the embodiment illustrated in FIG. 1 , the bioreactor 10 includes abottom port 26 connected to an effluent 28 for withdrawing materialsfrom the bioreactor continuously or periodically. Materials can bewithdrawn from the bioreactor 10 using any suitable method. Forinstance, in an alternative embodiment, an effluent can be removed fromthe bioreactor 10 from the top of the bioreactor using a dip tube. Inaddition, the bioreactor 10 includes a plurality of top ports, such asports 30, 32, and 34. Port 30 is in fluid communication with a firstfluid feed 36, port 32 is in fluid communication with a second feed 38and port 34 is in fluid communication with a third feed 40. The feeds36, 38 and 40 are for feeding various different materials to thebioreactor 10, such as a nutrient media. As used herein, a nutrientmedia refers to any fluid, compound, molecule, or substance that canincrease the mass of a bioproduct, such as anything that may be used byan organism to live, grow or otherwise add biomass. For example, anutrient feed can include a gas, such as oxygen or carbon dioxide thatis used for respiration or any type of metabolism. Other nutrient mediacan include carbohydrate sources. Carbohydrate sources include complexsugars and simple sugars, such as glucose, maltose, fructose, galactose,and mixtures thereof. A nutrient media can also include an amino acid.The amino acid may comprise, glycine, alanine, valine, leucine,isoleucine, methionine, proline, phenylalanine, tryptophan, serine,threonine, asparagine, glutamine, tyrosine, cysteine, lysine, arginine,histidine, aspartic acid and glutamic acid, single stereoisomersthereof, and racemic mixtures thereof. The term “amino add” can alsorefer to the known non-standard amino acids, e.g., 4-hydroxyproline,ε-N,N,N-trimethyllysine, 3-methylhistidine, 5-hydroxylysine,O-phosphoserine, γ-carboxyglutamate, γ-N-acetyllysine,ω-N-methylarginine, N-acetylserine, N,N,N-trimethylalanine,N-formylmethionine, γ-aminobutyric add, histamine, dopamine, thyroxine,citrulline, ornithine, β-cyanoalanine, homocysteine, azaserine, andS-adenosylmethionine. In some embodiments, the amino acid is glutamate,glutamine, lysine, tyrosine or valine.

The nutrient media can also contain one or more vitamins. Vitamins thatmay be contained in the nutrient media include group B vitamins, such asB12. Other vitamins include vitamin A, vitamin E, riboflavin, thiamine,biotin, and mixtures thereof. The nutrient media can also contain one ormore fatty adds and one or more lipids. For example, a nutrient mediafeed may include cholesterol, steroids, and mixtures thereof. A nutrientmedia may also supply proteins and peptides to the bioreactor. Proteinsand peptides include, for instance, albumin, transferrin, fibronectin,fetuin, and mixtures thereof. A growth medium within the presentdisclosure may also include growth factors and growth inhibitors, traceelements, inorganic salts, hydrolysates, and mixtures thereof. In oneembodiment, the growth medium can contain a serum, such as human serumor calf serum.

As shown in FIG. 1 , the bioreactor can be in communication withmultiple nutrient feeds. In this manner, a nutrient media can be fed tothe bioreactor containing only a single nutrient for better controllingthe concentration of the nutrient in the bioreactor during the process.In addition or alternatively, the different feed lines can be used tofeed gases and liquids separately to the bioreactor.

In addition to ports on the top and bottom of the bioreactor 10, thebioreactor can include ports located along the sidewall. For instance,the bioreactor 10 shown in FIG. 1 includes ports 44 and 46. The portslocated along the sidewall are optional. For instance, in an alternativeembodiment, monitoring of the cell culture can occur from the top of thebioreactor 10 using headplate ports.

In accordance with the present disclosure, port 46 is in communicationwith a parameter monitoring and control system that can maintain optimumconcentrations of one or more parameters in the bioreactor 10 forpropagating cell cultures or otherwise producing a bioproduct. In theembodiment illustrated in FIG. 1 , the system is designed to take inline measurements. In particular, measurements are made of the cellculture while the cell culture resides within the bioreactor 10, i.e.online. Alternatively, however, measurements can be taken at line or offline. For example, in one embodiment, the bioreactor 10 can be incommunication with a sampling station. Samples of the cell culture canbe fed to the sampling station for taking light scattering measurements.In still another embodiment, samples of the cell culture can be removedfrom the bioreactor and measured off line.

In the embodiment illustrated in FIG. 1 , port 46 is in communicationwith a light conveying device 50. The light conveying device 50 is incommunication with a light source 52, such as a coherent light source.In addition, the light conveying device 50 is in communication with aRaman spectrometer 54. The light source 52 is for exposing a cellculture within the bioreactor 10 to a beam of light. The light conveyingdevice 50 is then configured to convey scattered light reflected off ofthe cell culture to the Raman spectrometer 54 for determining theconcentration of one or more parameters within the bioreactor 10. TheRaman spectrometer 54 and/or the light source 52 can be in communicationwith a controller 60. The controller 60 can determine the concentrationof one or more parameters within the bioreactor 10 from the informationor data received from the Raman spectrometer 54 and, based on the data,control one or more feeds 36, 38, or 40 and/or control the effluent 28in order to maintain one or more parameters within preset concentrationlimits within the bioreactor 10.

The light source 52 in accordance with the present disclosure can emit acoherent light beam having a controlled wavelength or wavelength range.The light source 52, for instance, may comprise a laser, a lightemitting diode, or possibly a filament bulb in conjunction with variousfilters. The light source 52 can be selected based on various factorsincluding the biomaterials being present in the bioreactor 10. The lightsource 52, for instance, can be adapted to the geometry and sensitivityof the system and can be selected based upon the spectral properties ofthe biomaterials contained within the reactor. In one embodiment, thelight source 52 emits monochromatic light for irradiation of the cellculture within the bioreactor 10. In one embodiment, a single lightsource 52 may be used. Alternatively, however, the system can includemultiple light sources that all operate at the same wavelength or atdifferent wavelengths. In general, the light beam emitted by the lightsource 52 can have wavelengths in the visible spectrum, the IR spectrum,and/or the NIR spectrum. For instance, the wavelength of light cangenerally be greater than about 400 nm, such as greater than about 500nm, such as greater than about 600 nm, such as greater than about 700nm. The wavelength of the light being emitted by the light source 52 cangenerally be less than about 1500 nm, such as less than about 1200 nm,such as less than about 1000 nm, such as less than about 900 nm. In oneembodiment, the wavelength of the light source can be from about 700 nmto about 850 nm. In one particular embodiment, the light source emits alight beam that has a wavelength of 785 nm. Longer wavelengths of light,for instance, can decrease the intensity of Raman scattered radiation.

The light source 52 can optionally be coupled with one or more lenses,beam splitters, diffraction gratings, polarization filters, band passfilters, or other optical elements selected for illuminating the samplewithin the bioreactor in a desired manner.

The light source 52 can directly emit a beam of light onto a cellculture contained within the bioreactor 10. Alternatively, radiationfrom the light source 52 can be transmitted to the sample surface by wayof one or more light conveying devices 50, such as optical fibers. Theone or more optical fibers can be used to illuminate the surface of thesample continuously or intermittently. In addition, the one or moreoptical fibers can illuminate generally the same area of a cell cultureor can be positioned to irradiate the cell culture at differentlocations.

In one embodiment, one or more optical fibers used to illuminate thesample within the bioreactor are bundled together with one or moreoptical detection fibers that are used to collect radiation reflected,emitted, or scattered from the surface. The discreet bundles ofillumination and detection optical fibers can be directed to selectedareas of the sample surface. The illumination fibers in each bundle cantransmit light from light source 52 to the selected area of the surface.Light reflected, emitted, or scattered from that area of the surface canthen be collected by the detection fibers. Light transmitted by thedetection fibers in each bundle can be assessed in a combined ordiscrete fashion as desired.

The light conveying device 50 or optical fibers can optionally becoupled to one or more lenses, beam splitters, diffraction gratings,polarization filters, band pass filters, or other optical elements. Inone embodiment, for instance, the reflected or scattered light collectedby the light conveying device 50 can be in communication with aholographic notch filter.

In addition to a holographic notch filter, transmitted, reflected,emitted or scattered light from the illuminated sample can includevarious other optical elements to facilitate transmission of light andto measure intensity. For instance, other optical elements that can beincluded in the pathway include lenses, beam splitters, diffractiongratings, polarization filters, band pass filters, and the like. Whendetecting Raman-shifted radiation scattered by a sample, for instance,other suitable filters can include cut-off filters, a Fabry Perot angletuned filter, an acousto-optic tunable filter, a liquid crystal tunablefilter, a Lyot filter, an Evans split element liquid crystal tunablefilter, a Solc liquid crystal tunable filter, a liquid crystal FabryPerot tunable filter, and the like. Suitable interferometers that may beused include polarization-independent imaging interferometers, aMichelson interferometer, a Sagnac interferometer, a Twynam-greeninterferometer, a Mach-Zehnder interferometer, a tunable Fabry Perotinterferometer, and the like. In general, any suitable detector can beused in order to better identify the Raman-shifted scattered radiationreceived from the sample area.

As shown in FIG. 1 , the light conveying device 50 which may compriseone or more optical fibers is connected to the Raman spectrometer 54.The Raman spectrometer 54 includes a detector. For example, scatteredlight can be transmitted to the detector in a mappable or addressablefashion such that light transmitted from different assessed regions ofthe sample surface can be differentiated by the detector. Otherwise,light from discrete assessed regions of a sample surface can betransmitted separately to discrete portions of a detector having alinear or two-dimensional array of detector elements.

The Raman spectrometer 54 as shown in FIG. 1 can include the detector.During Raman spectroscopy, the intensity of scattered light is measuredand vibrational, rotational, and other low-frequency changes or shiftsare observed. In one embodiment, the light scattered from the sample orcell culture is fed through a filter, such as a holographic notchfilter, in order to only observe inelastic scattering of the light. Inthis manner, one can observe the shift in photons from the originalwavelength. For example, the interaction of the beam of light withchemical constituents within the cell culture results in laser photonsbeing shifted up or down. The shift in energy gives information aboutthe vibrational mode in the system and can be used to fingerprintdifferent parameters, such as the presence and concentration of variousmolecules.

In one embodiment of the present disclosure, the Raman spectrometer 54includes a wavelength separator and a CCD (charge coupled device) camerafor better measuring the photon shifts and the intensity of the photonshifts. In this manner, the Raman spectrometer 54 can detect one or moreparameters simultaneously within the bioreactor 10.

Raman spectroscopy can provide numerous advantages and benefits whenused in accordance with the present disclosure. For example, asdescribed above, Raman spectroscopy processes inelastic light scatteringin order to provide specific information as to the presence ofparticular molecular bonds within the sample. In this way, multipleparameters or components can be measured and monitored simultaneously.Raman spectroscopy is capable of not only identifying the presence ofparticular parameters, but also capable of providing informationregarding the concentration of those parameters. As shown in FIG. 1 ,Raman spectroscopy can also be used in line without having to remove asample of the cell culture from the bioreactor 10. Samples also do notrequire any dilution and the measurement is not impacted significantlyby the presence of water. In addition, measurements can be takencontinuously or with relatively short time intervals. For instance,measurements can be taken at least every 24 hours, such as at leastevery 20 hours, such as at least every 15 hours, such as at least every10 hours, such as at least every 8 hours, such as at least every 4hours, such as at least every 2 hours, such as at least every hour, suchas at least every 30 minutes, such as at least every 10 minutes, such asat least every 5 minutes.

Data from the Raman spectrometer 54 can be fed to the controller 60. Thecontroller 60 and/or the Raman spectrometer can calculate theconcentration of one or more parameters of a cell culture contained inthe bioreactor 10.

In one embodiment, the processes of the present disclosure can be usedto monitor and adjust one or more quality characteristics. For instance,one product quality characteristic that can be monitored and controlledis the glycosylation profile which impacts proteins. Another productquality attribute that can be monitored and varied is the charge variantprofile which can indicate the presence of impurities in the product.

Various different parameters can be monitored in accordance with thepresent disclosure. For example, in one embodiment, the concentration ofa nutrient can be monitored. Examples of nutrients that can be monitoredinclude, for instance, glucose, glutamine and/or glutamate.Alternatively, the monitored parameter may relate to the concentrationof a waste product. Waste products that can be monitored in accordancewith the present disclosure include lactate and ammonium. In addition,various growth and characteristics can be monitored. For example, growthcharacteristic parameters include the viable cell concentration and thetotal cell concentration. Finally, the monitored parameter can comprisethe concentration of the final product.

In accordance with the present disclosure, in one embodiment, thecontroller 50 can collect Raman spectra that covers expected processvariations that may occur within the bioreactor 10. The controller 60can then determine the concentration of the one or more parameters usingany suitable method. In one embodiment, for instance, the parameterconcentrations are measured by comparing the collected spectra with offline reference data. The controller 60 can also develop calibrationmodels using multivariate software. In one embodiment, the controllercan also include qualifying predictive models that are developed basedon independent data sets.

In one embodiment, the Raman spectra collected by the controller canundergo various statistical analysis or preprocessing prior todetermining parameter concentrations. For example, in one embodiment,standard normal variate can be applied to the Raman spectra in order toremove scattering effects. For example, in one embodiment, the standardnormal variate can calculate a mean and a standard deviation in order toproduce a probability density function. For example, in one particularembodiment, the standard normal variate may include an expected value 0with variance of 1.

In one embodiment, the standard normal variate can be used inconjunction with applying a first derivative and detrending. Detrending,for instance, can be used to reduce an upward baseline trend caused byfluorescence. After applying the standard normal variate, detrending,and applying the first derivative, a spectral range can then be selectedthat correlates to the parameter of interest. In one embodiment, theprocessed spectra can be modeled using partial least squares regression.

The controller 60 may comprise one or more programmable devices ormicroprocessors. As shown in FIG. 1 , the controller 60 can be incommunication with the one or more feeds 36, 38 and 40 and with one ormore effluents 28. The controller can be configured to increase ordecrease the flow of materials and substances into the bioreactor 10based upon the concentration of one or more parameters. For example, thecontroller 60 can analyze signals received from the Raman spectrometer54 and generate output signals capable of controlling one or more inputand/or output devices.

In one embodiment, the controller 60 can be configured to selectivelyincrease or decrease the flow of a parameter influencing substance intoor out of the bioreactor 10 based upon the measured concentration of aparameter using its Raman spectra. In this manner, the controller 60 canmaintain the concentration of the parameter within preset limits. Thecontroller 60 can operate in an open loop control system or can operatein a closed loop control system, where adjustments to input and/oroutput devices are completed automatically.

In one embodiment, the controller 60 monitors at least two parameterswithin the bioreactor 10. For instance, the controller 60 can monitor atleast three parameters, such as at least 4 parameters, such as at least5 parameters. For instance, in one embodiment, the controller 60 canmonitor from about 2 to about 10 parameters, such as from about 2 toabout 6 parameters.

In one embodiment, the controller can be programmed with a predictivemodel that can predict future concentrations of the one or moreparameters to ensure that optimal conditions remain within thebioreactor from the beginning of the process to the end of the process.Programming the controller 60 with a predictive model, for instance, incombination with continuous monitoring, provides potential feedbackcontrol for very complex solutions. Using a predictive model, especiallyin conjunction with monitoring more than one parameter, for instance,can capture as much variability as possible during the entire process ofpropagating the cell culture.

The predictive model can be created using a design of experimentsapproach that contains concentrations of desired parameters andassociated Raman spectra that covers as much of the process as possible.Further improvements can be obtained by spiking in parameters at varyingconcentrations and measuring the resulting spectra. In addition, furtherimprovements in predictive models can be obtained by forcibly breakingthe correlations that may be present. Inclusion of these data points inthe calibration model improves the predictive ability of the model forfuture data sets. Once the calibration and predictive models from Ramanspectra are developed, they can be used for process monitoring andfeedback control within the bioreactor. Of particular advantage, theprocess of the present disclosure allows for the predictive model to beused against many different cell cultures and in many different types ofbioreactors.

In one embodiment, the bioreactor 10 is for growing a cell culture in abatch process. Alternatively, the bioreactor 10 can be operated in aperfusion process, where fluids are continuously removed andreplenished. The amount of time the cell culture is propagated can varydepending upon various factors. In general, for instance, the cellculture can be propagated for a period of time of from about 6 days toabout 30 days, such as from about 8 days to about 20 days. In oneembodiment, concentration measurements of one or more parameters can beobtained in the initial stages of cell growth. For instance, Ramanspectra can be obtained for different parameters over the first 1 to 6days, such as over the first 2 to 4 days. The controller 60 can receivethis information and begin building predictive data that predicts futureconcentrations of each of the monitored parameters. After receiving theinformation for a period of time, the controller 60 can then selectivelyincrease or decrease a parameter influencing substance that may be fedor withdrawn from the bioreactor 10. For example, in one embodiment, thecontroller 60 can begin making selective adjustments to the bioreactorafter 2 to 4 days of receiving data and based upon how theconcentrations of the parameters fit within the predictive model.

For example, in one embodiment, the system can be configured to monitorglucose concentration in conjunction with at least one other parameter,such as lactate concentration. Based upon the monitored concentration ofboth parameters, the controller 60 can then automatically makeadjustments to the flow of one or more nutrient media into thebioreactor 10. The nutrient media, for instance, may contain glucose. Inthis manner, glucose concentrations can be maintained within presetparameters in conjunction with maintaining lactate concentrations withinpreset parameters. In one embodiment, for instance, glucose levels aremaintained so as to minimize fluctuations in lactate levels and maintainlactate levels below desired set points.

In an alternative embodiment, the controller can be used to control theeffluent rate in a continuous perfusion bioreactor to maintain parameterlevels below a desired set point, such as to maintain lactate levelsbelow a desired set point.

In addition to monitoring one or more parameters through Ramanspectroscopy, the controller can control various other processconditions. For instance, the controller can be in communication andcontrol thermocirculators, load cells, control pumps, and receiveinformation from various sensors and probes. For instance, thecontroller may control and/or monitor the pH, the oxygen tension,dissolved carbon dioxide, the temperature, the agitation conditions, thealkali condition, the pressure, foam levels, and the like. For example,based on pH readings from a pH probe, the controller may be configuredto regulate pH levels by adding requisite amounts of acid or alkali. Thecontroller may also use a carbon dioxide gas supply to decrease pH.Similarly, the controller can receive temperature information andcontrol fluids being feed to a water jacket surrounding the bioreactorfor increasing or decreasing temperature.

Many different cell cultures can be maintained or propagated using theprocess of the present disclosure. For instance, in one embodiment, thebioreactor can contain mammalian cells. Alternatively, the process ofthe present disclosure can be used to harvest cells for cell therapy.For example, in one embodiment, the bioreactor can contain stem cells, Tcells, immune cells, and the like.

Through the process of the present disclosure, cell cultures can begrown with excellent product characteristics. For example, cell culturescan be grown with excellent viability characteristics. For example,viability can be measured by dividing the viable cell count with thetotal cell count, which are two parameters that can both be measuredusing the Raman spectra. In accordance with the present disclosure, cellcultures can be grown in accordance with the present disclosure having aviability ratio as described above of greater than about 0.6, such asgreater than about 0.7, such as greater than about 0.8, such as greaterthan about 0.9. In fact, the viability ratio can be greater than about0.94, such as greater than about 0.96, such as greater than about 0.98.

The present disclosure may be better understood with reference to thefollowing examples.

Example No. 1

Three different CHOK1SV GS-KO™ cell lines producing different monoclonalantibodies (mAbs) were used during this study. Two cell lines were usedfor calibration model development and the remaining cell line was usedfor model qualification. All cell lines were cultured on platform mediaand feeds over a 15-day period. All cell cultures for the calibrationmodels were performed in four stirred tank reactors (STRs) with a 5liter working volume. Each calibration cell culture had two controlsoperating at a target residual glucose concentration of ≈3 g/L. Toexpand the operating range for glucose two STRs were operated with ≈1g/L extra initial glucose and maintained at a target residual glucoseconcentration of 1 g/L. Additionally, data from one round of abnormalcell culture in four STRs with a 5 liter working volume was included toaccount for metabolite and cell concentrations well outside of expectedranges for the platform process. For model qualification two STRs wereoperated with a 5 liter working volume, while one STR was operated witha 10 liter working volume to assess model scalability.

Offline Analytics

During culture, offline samples (≈20 mL) were taken twice daily fromeach culture for analysis of metabolites and cell growth. Offlineanalysis of glucose, lactate, glutamate, and ammonium were performedusing a NOVA Bioprofile 400 (NOVA Biomedical). Offline analysis of VCC(viable cell concentration) and TCC (total cell concentration) wasperformed using a Vi-Cell XR (Beckman Coulter). From day 4 of cultureonwards, twice daily aliquots K mL) were saved from cell culturesupernatant for product concentration analysis. Product concentrationwas analyzed via Protein A HPLC.

Inline Raman spectra were collected using a RAMANRXN2™ (RXN2) systemfrom Kaiser Optical System, Inc. with a 785 nm laser using four 420 mmbio-optic probes (1 probe per bioreactor). Raman spectra were generatedfrom 150 scans with a 5 second exposure time for a total analysis timeof ≈12.5 minutes. For cell cultures used to build the calibration set,inline Raman spectra were collected twice daily from each bioreactor andcoincided with measurement of the offline samples. Total collection timefor inline Raman spectra from all four probes was ≈1 hour.

For model qualification inline Raman spectra were collected every threehours and compared to the offline measurements. The extra spectracollected in between offline sampling during the qualification runallowed for trending the measured parameters over time and offeredadditional insight into the ability of the developed models to monitorwhere matched with their respective offline measurements prior to beingimported into SIMCA v13.0.3. Next regions outside the fingerprint regionwere removed (<500 cm-1 and >1700 cm-1 for most models) to prevent thesesignals being given inappropriate weight in the resulting models (FIG.2A). If left in these regions of noise can mask the impact of Ramanregions correlated to changes in metabolite concentration (e.g.,residual glucose concentration) hindering model robustness. Afterspectral trimming, different combinations of spectral pre-processingwere applied and PLS models were constructed. The raw spectra from thedifferent cell culture runs showed a baseline shift over the course ofthe experiments which was reduced by application of a 1st derivativefilter (FIG. 2B). Moreover, derivative filters were required to createthe best models for all parameters in this study. It should be notedthat the actual spectral pre-processing utilized is parameter dependent.The resulting model statistics were compared and the models with thelowest RMSEE/RMSECV were saved for use in monitoring the qualificationset.

Model Calibration

Offline data from 12 cell culture runs utilizing a platform process in 5liter STRs were combined with their respective Raman spectra and used toproduce calibration models for glucose, glutamate, lactate, ammonium,viable cell concentration (VCC), total cell concentration (TCC), andproduct concentration (Table 1). In general, the models had lowRMSEE/RMSECV values with R2Y>0.90 for all parameters. Additionally, allmodels had a relatively low number of latent variables (LV's), with theexception of ammonium. The high number of LV's for the ammonium modelmay indicate that the model is over fit for this parameter (R2cv=0.89).In building the models offline data and spectra were collected thatcovered ranges outside of normal operating conditions for the platformprocess. This was done to avoid creating situations where the modelswould be required to extrapolate. Calibration model plots for predictedglucose, glutamate, lactate, ammonium, VCC, TCC, and productconcentrations versus their respective offline reference methods,indicate that in general all models correlate well with the measuredvalues (FIG. 3 ).

TABLE 1 Calibration Model Statistics Parameter N LV R²Y R² _(cv) RMSEERMSECV Range Glucose (g/L) 344 5 0.99 0.99 0.33 0.39 0.00-11.14 Lactate(g/L 344 5 0.97 0.96 0.34 0.36 0.00-12.47 Glutamate (mM) 342 6 0.96 0.940.71 0.79 0.00-19.92 Ammonium 344 9 0.94 0.89 0.015 0.017 0.000-0.208 VCC (×10⁶ 344 6 0.97 0.97 2.24 2.40 0.43-47.10 TCC (×10⁶ 344 6 0.98 0.972.21 2.34 0.44-48.00 Product 267 6 0.99 0.98 0.48 0.49 0.04-10.15

Model Qualification & Scalability

Models were qualified using a cell line not included in the calibrationmodels. All qualification runs were performed under normal operatingconditions for the platform process. To investigate the potentialscalability of the developed model a 10 liter culture was alsoperformed. The resulting prediction versus offline reference methodprofiles for all three qualification runs are shown in FIG. 4 . Theerror bars for predicted values are ±the RMSEP for each culture run. Theerror bars for the offline reference method are ±precision as specifiedby the vendor. All models were able to monitor changes in the desiredparameters with relatively low RMSEP values. (Table 2).

TABLE 2 Summary Model Statistics for Qualification Round ConcentrationR² _(p) RMSEP Parameter N Range i ii iii i ii iii Glucose (g/L) 87 0.44-10.12 0.99 0.98 0.99 0.47 0.43 0.41 Lactate (g/L) 87 0.00-3.760.96 0.97 0.94 0.30 0.22 0.18 Glutamate (mM) 87 0.00-5.34 0.60 0.18 0.560.97 1.63 0.89 Ammonium (g/L) 87 0.009-0.242 0.88 0.81 0.93 0.02 0.040.02 VCC (×10⁶ cells/mL) 87  0.51-34.87 0.98 0.99 0.99 1.90 2.32 1.48TCC (×10⁶ cells/mL) 87  0.51-35.58 0.98 0.99 0.99 2.25 1.97 1.34 ProductConcentration (g/L) 66 0.00-4.70 0.94 0.94 0.99 1.21 0.75 0.98 i = 5 LRun 1, ii = 5 L Run 2, and iii = 10 L Run

The predictive model developed for glucose was found to satisfactorilypredict concentrations of glucose over all three cell cultures with anaverage RMSEP of 0.44 g/L versus an average process glucoseconcentration of 5.18 g/L. Importantly the developed models were capableof monitoring changes in residual glucose concentration as cultures werefed a concentrated glucose feed (FIG. 4A). Careful glucose feeding toCHO cell cultures has been shown to increase product quality. Thepercent glycation can be reduced by approximately half through thecontrol of residual glucose, made possible through inline Ramanmonitoring.

The predictive model developed for lactate satisfactorily monitoredchanges in lactate concentration across all three cultures with anaverage RMSEP of 0.23 g/L versus an average process lactateconcentration of 0.81 g/L. A slight over prediction of lactate wasobserved towards the end of one culture (FIG. 4Ci). This may be due tothe measured values being below the lower range of offline referencemethod, which defaults to a value of 0 g/L. Importantly, it was able tomeasure a shift in lactate concentration outside of expected ranges forthe platform process (FIG. 4Cii). This is attributed to utilizing acalibration dataset with a concentration range for lactate that was welloutside of what is typically observed for this process (Table 1). As PLSmodels are unable to extrapolate data, ensuring that any potentialexcursion is included during calibration helps to increase therobustness of the model to atypical culture performance (FIG. 4Cii).

The predictive model developed for ammonium was capable of monitoringchanges in the ammonium concentration across all cultures with anaverage RMSEP of 0.03 g/L versus an average process ammoniumconcentration of 0.09 g/L. A slight under prediction was observed forone cell culture towards the end of the run (FIG. 4Dii). This is likelydue to the concentration range for ammonium in the calibration culturesbeing <0.2 g/L causing the model to extrapolate these values (Table 1,FIG. 4D). Inclusion of more offline reference measurements covering abroader range of ammonium concentrations should allow a more robustmodel with an improved ability to monitor changes in ammoniumconcentration.

The predictive models developed for VCC and TCC were capable ofmonitoring changes across all three qualification cultures with averageRMSEP's of 1.90×10⁶ and 1.85×10⁶ cells/mL versus average process valuesof 1.80×10⁶ and 1.86×10⁶ cells/mL respectively. Importantly the modelswere able to predict VCC and TCC within the errors of the offlinereference method for the majority of the qualification cultures.Discrepancies between predicted and measured VCC during the stationaryphase of growth may be a result of dilution errors associated withsample preparation for Vi Cell XR analysis at these high cellconcentrations. Despite these errors the developed model for VCC isconsidered to be suitable to provide online control of nutrient feeds tofed-batch and perfusion cell cultures, which historically have beenadjusted based on daily VCC measurements and using inline capacitanceprobes. While capacitance based predictions were shown to be effective,engineering constraints limit the number of available probe ports intypical GMP bioreactor vessels. In this case the use of a single probeto monitor multiple parameters for feedback control of nutrient feeds isdesirable, giving inline Raman probes a clear advantage.

The predictive model developed for glutamate monitored changes acrossthe qualification cultures with an average RMSEP of 1.61 mM versus anaverage process glutamate concentration of 1.64 mM (FIG. 4B). Any errorin the glutamate model may be due to the measurement error associatedwith glutamate detection from the NOVA Bioprofile 400. Accuratemeasurement by the offline reference method may be required to buildrobust PLS models from inline Raman spectra. As such, the glutamatemodel could be improved through the use of a more accurate referencemethod for this component.

The predictive model developed for product concentration had an averageRMSEP of 0.98 g/L versus an average product concentration of 1.74 g/Lfor the process (FIG. 4 ). Any errors in the prediction of productconcentration may be attributed to a lack of data from the early timepoints of cell culture used for model development. At the beginning ofcell culture, the model predicted the presence of a substantial amountof product when very little is expected to be present (FIG. 4G). Thisdiscrepancy could be due to the fact that the calibration model does notcontain offline product concentration data prior to day 4 of culture.The inclusion of such data may enable the development of an improvedpredictive model. It is possible that any prediction errors later in theculture indicate that the model built from the Raman spectra is notdirectly measuring changes in product concentration, but estimatingproduct concentration based on other factors that are correlated withthis parameter. For instance, product concentration is correlated withtime and VCC, however this correlation varies significantly between celllines and products. If the model was estimating product concentrationfrom correlations with time and VCC this may help explain why the modelwas slightly off when predicting product concentration at later timepoints, for a cell line not included in the calibration model (FIG. 4G).Additionally, different mAbs can have different amino acid sequenceswhich may yield different Raman signals. This in turn could impact theability of the developed model to predict a different mAbs concentrationduring cell culture.

Inline Raman spectra from four different Raman probes were used tocreate calibration models from offline reference measurements of twoCHOK1SV GS-KO™ cell lines cultured on a platform media that covered theexpected variation in the process metabolites, cell growth, and productconcentration. Calibration models were capable of predicting theconcentrations of glucose, lactate, ammonium, viable cell concentrationand total cell concentration within the error of the offline referencemethods for three rounds of qualification cell culture at differentscales. Furthermore, these generic models were found to be independentof the CHOK1SV GS-KO™ cell line used for these process parameters.Robust models may be satisfactorily made for glutamate and productconcentration with small changes. More sensitive offline methods andinclusion of more data could improve the models for glutamate andproduct concentration respectively.

Through the process of the present disclosure, feedback control ofnutrient feeds allows for careful monitoring and automatic adjustment ofcell cultures for improving output in quality. Referring to FIG. 5 ,glucose concentration is illustrated over time. As shown, the process ofthe present disclosure is capable of maintaining glucose levels withincarefully controlled limits, especially in comparison to prior systemsthat simply adjust the feed rate of glucose based on daily measurements.As shown in FIG. 5 , the continued adjustment of the glucose feed from aRaman signal leads to a more consistent glucose profile around thetarget for the majority of the culture.

Control of a complex nutrient feed tied to variable cell concentrationin accordance with the present disclosure is also shown in FIG. 6 .

Attached also is further information and illustrations regarding thesystem and process of the present disclosure.

The development of generic models demonstrates that it is possible toapply Raman spectroscopy for measuring key culture metabolites in anindustrial platform process. Moreover, these models can help realize thepotential of automated process control in clinical manufacturingoperations where processes are run only once or twice at GMP scale.Finally, the successful monitoring of the highlighted parameters usingthe developed models should enable the use of inline Raman probes forcontinuous monitoring and control of nutrient feeds to provide morerobust and consistent processes at both the clinical and commercialmanufacturing scales.

Example No. 2

Human mesenchymal stem cells (MSCs) were cultured in a stirred tankbioreactor containing microcarriers. The culture period totaled eightdays. For the first four days, cells were incubated in MSC media at aglucose concentration of 2 g/L. Starting on day 4 of culture, andthrough day 8, continuous perfusion was initiated, replacing the oldmedia with fresh MSC media at a glucose concentration of 4 g/L. Over thecourse of these eight days, samples were periodically drawn from thebioreactors and glucose, lactate, and ammonium concentrations weremeasured from fresh samples using the Nova BioProfile Flex instrument.Inline Raman spectra were collected using a RamanRXN2™ system fromKaiser Optical System, Inc. with a 785 nm laser scanning through aspecific spectral range, with 420-2465 cm⁻¹ used for modeling.

Model Calibration:

Four 5 L bioreactors containing 3.5 L of culture were run for modelcalibration. The Raman instrument used a 10 second exposure time, andfor each measurement collected 75 accumulations. Measurements were takenevery 30 minutes. A total of 47 (N=47) samples were measured on the Novafrom the four runs.

Preprocessing was performed by four steps: applying the Standard NormalVariate (SNV), detrending, applying the first derivative, and choosing aspectral range correlating to the molecule of interest. FIGS. 7-10illustrate these preprocessing steps as applied to one of thequalification runs. FIG. 7 shows the raw, unprocessed spectra. FIG. 8shows the same spectra as FIG. 7 , but after applying SNV. FIG. 9 showsthe same spectra after SNV and detrending. FIG. 10 shows the samespectra after SNV, detrending, and applying a 1st derivative.

The processed spectra were then modeled using partial least squaresregression (PLS-R). Table 1 summarizes the statistics of the resultingmodels. Only 17 samples of ammonium were included in the PLS-R modelbecause they had values of zero.

TABLE 1 Model calibration statistics Parameter N LV R²Y R² _(CV) RMSEERMSECV Range Glucose (g/L) 47 3 0.92 0.90 0.25 0.29 0.15-4.02 Lactate(g/L) 47 3 0.96 0.95 0.13 0.17 0.10-1.86 Ammonium (mmol/L) 17 4 0.940.91 0.16 0.20 0.02-2.35

FIGS. 11-13 depict the increasing percentage of the Y variance explainedby each additional factor (latent variables) in the model for each ofthe three modeled molecules. FIGS. 14-16 depict Hotelling's T² Ellipsesfor each of the three modeled molecules, which visualizes which pointsin the calibration set are inside versus outside the 95% confidenceinterval.

The conclusion from these statistics is that the model is a good fit forglucose, lactate and ammonium within relatively small error relative tothe concentration range intended to be measured in the processes.

Model Qualification:

Seven 3 L bioreactors with working volumes ranging from 2-3 L were runfor model qualification. An average of 7 samples were taken from eachbioreactor run, totaling 50 samples. A small number of samples aremissing reference values for lactate and ammonium, this was due totechnical issues with the Nova for those particular samples and somezero values. The MSC process for the qualification runs was largely thesame as the process used for calibration, with a few small exceptions:different lots of media were used, the qualification runs were performedusing gentamicin, and the qualification runs include the addition ofantifoam during part of the process. There was a difference in Ramanspectra caused by the presence of gentamicin, this was corrected for bychemometrics.

Table 2 shows the statistics from the qualification runs. In six ofseven runs, glucose was predicted within the range forecast by thecalibration statistics, and in the seventh run the RMSEP value was 0.33,only slightly higher than the RMSECV of 0.29. In six of seven runs,lactate was predicted within the range forecast by the calibrationstatistics, and in the seventh run the RMSEP value was 0.19, onlyslightly higher than the RMSECV of 0.17. In all seven runs, ammonium waspredicted outside the range forecast by the calibration statistics,although still within 0.5 g/L of the true value, with the exception ofthe first run. This was likely due to a lower number of ammonium samplesin the calibration set. Predictive plots for all seven runs, for allthree molecules, can be seen in FIGS. 17-19 .

TABLE 2 RMSEP Concentration 3 L - 3 L - 3 L - 3 L - 3 L - 3 L - 3 L -Parameter N range 33 35A 35B 36A 36B 37A 37B Glucose (g/L) 50 1.65-4.280.29 0.18 0.18 0.21 0.20 0.33 0.11 Lactate (g/L) 45 0.02-1.04 0.07 0.100.16 0.08 0.15 0.12 0.19 Ammonium (mmol/L) 48 0.78-2.33 0.53 0.43 0.360.23 0.25 0.21 0.26 R² _(p) Concentration 3 L - 3 L - 3 L - 3 L - 3 L -3 L - 3 L - Parameter N range 33 35A 35B 36A 36B 37A 37B Glucose (g/L)50 1.65-4.28 0.98 0.96 0.98 1.00 0.99 0.99 0.99 Lactate (g/L) 450.02-1.04 0.90 0.96 0.90 0.94 0.84 0.85 0.75 Ammonium (mmol/L) 480.78-2.33 0.19 0.18 0.24 0.71 0.98 0.68 0.55

Automated Raman-Driven Control:

One run was performed to test automated Raman-driven control. Thefrequency of measurements was increased, resulting in a measurementevery 10 minutes. Prior to conducting this run, the Raman system wasintegrated into the bioreactor controller such that it was possible todefine a set point for any of the molecules measured via Raman.

In this run, glucose was chosen to illustrate this. MSCs were initiallycultured at 2 g/L glucose FIG. 20 . On day 4, the glucose concentrationwas modified by defining a set point of 4 g/L. The controller wasconnected to a concentrated glucose feed, which, through a customfeedback function, was automatically pumped into the bioreactor—pumpactivity is indicated by red spikes in FIG. 20 . Throughout theremaining 4 days of culture, glucose was fed in automatically in orderto maintain the set point.

The devices, facilities and methods described herein are suitable forculturing any desired cell line including prokaryotic and/or eukaryoticcell lines. Further, in embodiments, the devices, facilities and methodsare suitable for culturing suspension cells or anchorage-dependent(adherent) cells and are suitable for production operations configuredfor production of pharmaceutical and biopharmaceutical products—such aspolypeptide products, nucleic acid products (for example DNA or RNA), orcells and/or viruses such as those used in cellular and/or viraltherapies.

In embodiments, the cells express or produce a product, such as arecombinant therapeutic or diagnostic product. As described in moredetail below, examples of products produced by cells include, but arenot limited to, antibody molecules (e.g., monoclonal antibodies,bispecific antibodies), antibody mimetics (polypeptide molecules thatbind specifically to antigens but that are not structurally related toantibodies such as e.g. DARPins, affibodies, adnectins, or IgNARs),fusion proteins (e.g., Fc fusion proteins, chimeric cytokines), otherrecombinant proteins (e.g., glycosylated proteins, enzymes, hormones),viral therapeutics (e.g., anti-cancer oncolytic viruses, viral vectorsfor gene therapy and viral immunotherapy), cell therapeutics (e.g.,pluripotent stem cells, mesenchymal stem cells and adult stem cells),vaccines or lipid-encapsulated particles (e.g., exosomes, virus-likeparticles), RNA (such as e.g. siRNA) or DNA (such as e.g. plasmid DNA),antibiotics or amino acids. In embodiments, the devices, facilities andmethods can be used for producing biosimilars.

As mentioned, in embodiments, devices, facilities and methods allow forthe production of eukaryotic cells, e.g., mammalian cells or lowereukaryotic cells such as for example yeast cells or filamentous fungicells, or prokaryotic cells such as Gram-positive or Gram-negative cellsand/or products of the eukaryotic or prokaryotic cells, e.g., proteins,peptides, antibiotics, amino acids, nucleic acids (such as DNA or RNA),synthesised by the eukaryotic cells in a large-scale manner. Unlessstated otherwise herein, the devices, facilities, and methods caninclude any desired volume or production capacity including but notlimited to bench-scale, pilot-scale, and full production scalecapacities.

Moreover and unless stated otherwise herein, the devices, facilities,and methods can include any suitable reactor(s) including but notlimited to stirred tank, airlift, fiber, microfiber, hollow fiber,ceramic matrix, fluidized bed, fixed bed, and/or spouted bedbioreactors. As used herein, “reactor” can include a fermentor orfermentation unit, or any other reaction vessel and the term “reactor”is used interchangeably with “fermentor.” For example, in some aspects,an example bioreactor unit can perform one or more, or all, of thefollowing: feeding of nutrients and/or carbon sources, injection ofsuitable gas (e.g., oxygen), inlet and outlet flow of fermentation orcell culture medium, separation of gas and liquid phases, maintenance oftemperature, maintenance of oxygen and CO2 levels, maintenance of pHlevel, agitation (e.g., stirring), and/or cleaning/sterilizing. Examplereactor units, such as a fermentation unit, may contain multiplereactors within the unit, for example the unit can have 1, 2, 3, 4, 5,10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, or 100, or morebioreactors in each unit and/or a facility may contain multiple unitshaving a single or multiple reactors within the facility. In variousembodiments, the bioreactor can be suitable for batch, semi fed-batch,fed-batch, perfusion, and/or a continuous fermentation processes. Anysuitable reactor diameter can be used. In embodiments, the bioreactorcan have a volume between about 100 mL and about 50,000 L. Non-limitingexamples include a volume of 100 mL, 250 mL, 500 mL, 750 mL, 1 liter, 2liters, 3 liters, 4 liters, 5 liters, 6 liters, 7 liters, 8 liters, 9liters, 10 liters, 15 liters, 20 liters, 25 liters, 30 liters, 40liters, 50 liters, 60 liters, 70 liters, 80 liters, 90 liters, 100liters, 150 liters, 200 liters, 250 liters, 300 liters, 350 liters, 400liters, 450 liters, 500 liters, 550 liters, 600 liters, 650 liters, 700liters, 750 liters, 800 liters, 850 liters, 900 liters, 950 liters, 1000liters, 1500 liters, 2000 liters, 2500 liters, 3000 liters, 3500 liters,4000 liters, 4500 liters, 5000 liters, 6000 liters, 7000 liters, 8000liters, 9000 liters, 10,000 liters, 15,000 liters, 20,000 liters, and/or50,000 liters. Additionally, suitable reactors can be multi-use,single-use, disposable, or non-disposable and can be formed of anysuitable material including metal alloys such as stainless steel (e.g.,316 L or any other suitable stainless steel) and Inconel, plastics,and/or glass.

In embodiments and unless stated otherwise herein, the devices,facilities, and methods described herein can also include any suitableunit operation and/or equipment not otherwise mentioned, such asoperations and/or equipment for separation, purification, and isolationof such products. Any suitable facility and environment can be used,such as traditional stick-built facilities, modular, mobile andtemporary facilities, or any other suitable construction, facility,and/or layout. For example, in some embodiments modular clean-rooms canbe used. Additionally and unless otherwise stated, the devices, systems,and methods described herein can be housed and/or performed in a singlelocation or facility or alternatively be housed and/or performed atseparate or multiple locations and/or facilities.

By way of non-limiting examples and without limitation, U.S. PublicationNos. 2013/0280797; 2012/0077429; 2011/0280797; 2009/0305626; and U.S.Pat. Nos. 8,298,054; 7,629,167; and 5,656,491, which are herebyincorporated by reference in their entirety, describe examplefacilities, equipment, and/or systems that may be suitable.

In embodiments, the cells are eukaryotic cells, e.g., mammalian cells.The mammalian cells can be for example human or rodent or bovine celllines or cell strains. Examples of such cells, cell lines or cellstrains are e.g. mouse myeloma (NSO)-cell lines, Chinese hamster ovary(CHO)-cell lines, HT1080, H9, HepG2, MCF7, MDBK Jurkat, NIH3T3, PC12,BHK (baby hamster kidney cell), VERO, SP2/0, YB2/0, Y0, C127, L cell,COS, e.g., COS 1 and COS 7, QC1-3, HEK-293, VERO, PER.C6, HeLA, EBI,EB2, EB3, oncolytic or hybridoma-cell lines. Preferably the mammaliancells are CHO-cell lines. In one embodiment, the cell is a CHO cell. Inone embodiment, the cell is a CHO-K1 cell, a CHO-K1 SV cell, a DG44 CHOcell, a DUXB11 CHO cell, a CHOS, a CHO GS knock-out cell, a CHO FUT8 GSknock-out cell, a CHOZN, or a CHO-derived cell. The CHO GS knock-outcell (e.g., GSKO cell) is, for example, a CHO-K1 SV GS knockout cell.The CHO FUT8 knockout cell is, for example, the Potelligent® CHOK1 SV(Lonza Biologics, Inc.). Eukaryotic cells can also be avian cells, celllines or cell strains, such as for example, EBx® cells, EB14, EB24,EB26, EB66, or EBvl3.

In one embodiment, the eukaryotic cells are stem cells. The stem cellscan be, for example, pluripotent stem cells, including embryonic stemcells (ESCs), adult stem cells, induced pluripotent stem cells (iPSCs),tissue specific stem cells (e.g., hematopoietic stem cells) andmesenchymal stem cells (MSCs).

In one embodiment, the cells are for cell therapy.

In one embodiment, the cells may include T cells, or immune cells. Forinstance, the cells can include B cells, natural killer cells, dendriticcells, tumor infiltrating lymphocytes, monocytes, megakaryocytes, or thelike.

In one embodiment, the cell is a differentiated form of any of the cellsdescribed herein. In one embodiment, the cell is a cell derived from anyprimary cell in culture.

In embodiments, the cell is a hepatocyte such as a human hepatocyte,animal hepatocyte, or a non-parenchymal cell. For example, the cell canbe a plateable metabolism qualified human hepatocyte, a plateableinduction qualified human hepatocyte, plateable Qualyst TransporterCertified™ human hepatocyte, suspension qualified human hepatocyte(including 10-donor and 20-donor pooled hepatocytes), human hepatickupffer cells, human hepatic stellate cells, dog hepatocytes (includingsingle and pooled Beagle hepatocytes), mouse hepatocytes (including CD-1and C57Bl/6 hepatocytes), rat hepatocytes (including Sprague-Dawley,Wistar Han, and Wistar hepatocytes), monkey hepatocytes (includingCynomolgus or Rhesus monkey hepatocytes), cat hepatocytes (includingDomestic Shorthair hepatocytes), and rabbit hepatocytes (including NewZealand White hepatocytes). Example hepatocytes are commerciallyavailable from Triangle Research Labs, LLC, 6 Davis Drive ResearchTriangle Park, N.C., USA 27709.

In one embodiment, the eukaryotic cell is a lower eukaryotic cell suchas e.g. a yeast cell (e.g., Pichia genus (e.g. Pichia pastoris, Pichiamethanolica, Pichia kluyveri, and Pichia angusta), Komagataella genus(e.g. Komagataella pastoris, Komagataella pseudopastoris or Komagataellaphaffii), Saccharomyces genus (e.g. Saccharomyces cerevisae, cerevisiae,Saccharomyces kluyveri, Saccharomyces uvarum), Kluyveromyces genus (e.g.Kluyveromyces lactis, Kluyveromyces marxianus), the Candida genus (e.g.Candida utilis, Candida cacaoi, Candida boidinii,), the Geotrichum genus(e.g. Geotrichum fermentans), Hansenula polymorpha, Yarrowia lipolytica,or Schizosaccharomyces pombe. Preferred is the species Pichia pastoris.Examples for Pichia pastoris strains are X33, GS115, KM71, KM71H; andCBS7435.

In one embodiment, the eukaryotic cell is a fungal cell (e.g.Aspergillus (such as A. niger, A. fumigatus, A. orzyae, A. nidula),Acremonium (such as A. thermophilum), Chaetomium (such as C.thermophilum), Chrysosporium (such as C. thermophile), Cordyceps (suchas C. militaris), Corynascus, Ctenomyces, Fusarium (such as F.oxysporum), Glomerella (such as G. graminicola), Hypocrea (such as H.jecorina), Magnaporthe (such as M. orzyae), Myceliophthora (such as M.thermophile), Nectria (such as N. heamatococca), Neurospora (such as N.crassa), Penicillium, Sporotrichum (such as S. thermophile), Thielavia(such as T. terrestris, T. heterothallica), Trichoderma (such as T.reesei), or Verticillium (such as V. dahlia)).

In one embodiment, the eukaryotic cell is an insect cell (e.g., Sf9,Mimic™ Sf9, Sf21, High Five™ (BT1-TN-5B1-4), or BT1-Ea88 cells), analgae cell (e.g., of the genus Amphora, Bacillariophyceae, Dunaliella,Chlorella, Chlamydomonas, Cyanophyta (cyanobacteria), Nannochloropsis,Spirulina, or Ochromonas), or a plant cell (e.g., cells frommonocotyledonous plants (e.g., maize, rice, wheat, or Setaria), or froma dicotyledonous plants (e.g., cassava, potato, soybean, tomato,tobacco, alfalfa, Physcomitrella patens or Arabidopsis).

In one embodiment, the cell is a bacterial or prokaryotic cell.

In embodiments, the prokaryotic cell is a Gram-positive cells such asBacillus, Streptomyces Streptococcus, Staphylococcus or Lactobacillus.Bacillus that can be used is, e.g. the B. subtilis, B.amyloliquefaciens, B. licheniformis, B. natto, or B. megaterium. Inembodiments, the cell is B. subtilis, such as B. subtilis 3NA and B.subtilis 168. Bacillus is obtainable from, e.g., the Bacillus GeneticStock Center, Biological Sciences 556, 484 West 12th Avenue, ColumbusOhio 43210-1214.

In one embodiment, the prokaryotic cell is a Gram-negative cell, such asSalmonella spp. or Escherichia coli, such as e.g., TG1, TG2, W3110, DH1,DHB4, DH5a, HMS 174, HMS174 (DE3), NM533, C600, HB101, JM109, MC4100,XL1-Blue and Origami, as well as those derived from E. coli B-strains,such as for example BL-21 or BL21 (DE3), all of which are commerciallyavailable.

Suitable host cells are commercially available, for example, fromculture collections such as the DSMZ (Deutsche Sammlung vonMikroorganismen and Zellkulturen GmbH, Braunschweig, Germany) or theAmerican Type Culture Collection (ATCC).

In embodiments, the cultured cells are used to produce proteins e.g.,antibodies, e.g., monoclonal antibodies, and/or recombinant proteins,for therapeutic use. In embodiments, the cultured cells producepeptides, amino acids, fatty acids or other useful biochemicalintermediates or metabolites. For example, in embodiments, moleculeshaving a molecular weight of about 4000 daltons to greater than about140,000 daltons can be produced. In embodiments, these molecules canhave a range of complexity and can include posttranslationalmodifications including glycosylation.

In embodiments, the protein is, e.g., BOTOX, Myobloc, Neurobloc, Dysport(or other serotypes of botulinum neurotoxins), alglucosidase alpha,daptomycin, YH-16, choriogonadotropin alpha, filgrastim, cetrorelix,interleukin-2, aldesleukin, teceleulin, denileukin diftitox, interferonalpha-n3 (injection), interferon alpha-n1, DL-8234, interferon, Suntory(gamma-1a), interferon gamma, thymosin alpha 1, tasonermin, DigiFab,ViperaTAb, EchiTAb, CroFab, nesiritide, abatacept, alefacept, Rebif,eptoterminalfa, teriparatide (osteoporosis), calcitonin injectable (bonedisease), calcitonin (nasal, osteoporosis), etanercept, hemoglobinglutamer 250 (bovine), drotrecogin alpha, collagenase, carperitide,recombinant human epidermal growth factor (topical gel, wound healing),DWP401, darbepoetin alpha, epoetin omega, epoetin beta, epoetin alpha,desirudin, lepirudin, bivalirudin, nonacog alpha, Mononine, eptacogalpha (activated), recombinant Factor VIII+VWF, Recombinate, recombinantFactor VIII, Factor VIII (recombinant), Alphnmate, octocog alpha, FactorVIII, palifermin, lndikinase, tenecteplase, alteplase, pamiteplase,reteplase, nateplase, monteplase, follitropin alpha, rFSH, hpFSH,micafungin, pegfilgrastim, lenograstim, nartograstim, sermorelin,glucagon, exenatide, pramlintide, iniglucerase, galsulfase, Leucotropin,molgramostim, triptorelin acetate, histrelin (subcutaneous implant,Hydron), deslorelin, histrelin, nafarelin, leuprolide sustained releasedepot (ATRIGEL), leuprolide implant (DUROS), goserelin, Eutropin, KP-102program, somatropin, mecasermin (growth failure), enlfavirtide,Org-33408, insulin glargine, insulin glulisine, insulin (inhaled),insulin lispro, insulin deternir, insulin (buccal, RapidMist),mecasermin rinfabate, anakinra, celmoleukin, 99 mTc-apcitide injection,myelopid, Betaseron, glatiramer acetate, Gepon, sargramostim,oprelvekin, human leukocyte-derived alpha interferons, Bilive, insulin(recombinant), recombinant human insulin, insulin aspart, mecasenin,Roferon-A, interferon-alpha 2, Alfaferone, interferon alfacon-1,interferon alpha, Avonex′ recombinant human luteinizing hormone, dornasealpha, trafermin, ziconotide, taltirelin, diboterminalfa, atosiban,becaplermin, eptifibatide, Zemaira, CTC-111, Shanvac-B, HPV vaccine(quadrivalent), octreotide, lanreotide, ancestirn, agalsidase beta,agalsidase alpha, laronidase, prezatide copper acetate (topical gel),rasburicase, ranibizumab, Actimmune, PEG-Intron, Tricomin, recombinanthouse dust mite allergy desensitization injection, recombinant humanparathyroid hormone (PTH) 1-84 (sc, osteoporosis), epoetin delta,transgenic antithrombin III, Granditropin, Vitrase, recombinant insulin,interferon-alpha (oral lozenge), GEM-21S, vapreotide, idursulfase,omnapatrilat, recombinant serum albumin, certolizumab pegol,glucarpidase, human recombinant Cl esterase inhibitor (angioedema),lanoteplase, recombinant human growth hormone, enfuvirtide (needle-freeinjection, Biojector 2000), VGV-1, interferon (alpha), lucinactant,aviptadil (inhaled, pulmonary disease), icatibant, ecallantide,omiganan, Aurograb, pexigananacetate, ADI-PEG-20, LDI-200, degarelix,cintredelinbesudotox, Favld, MDX-1379, ISAtx-247, liraglutide,teriparatide (osteoporosis), tifacogin, AA4500, T4N5 liposome lotion,catumaxomab, DWP413, ART-123, Chrysalin, desmoteplase, amediplase,corifollitropinalpha, TH-9507, teduglutide, Diamyd, DWP-412, growthhormone (sustained release injection), recombinant G-CSF, insulin(inhaled, AIR), insulin (inhaled, Technosphere), insulin (inhaled,AERx), RGN-303, DiaPep277, interferon beta (hepatitis C viral infection(HCV)), interferon alpha-n3 (oral), belatacept, transdermal insulinpatches, AMG-531, MBP-8298, Xerecept, opebacan, AIDSVAX, GV-1001,LymphoScan, ranpirnase, Lipoxysan, lusupultide, MP52(beta-tricalciumphosphate carrier, bone regeneration), melanoma vaccine,sipuleucel-T, CTP-37, Insegia, vitespen, human thrombin (frozen,surgical bleeding), thrombin, TransMlD, alfimeprase, Puricase,terlipressin (intravenous, hepatorenal syndrome), EUR-1008M, recombinantFGF-I (injectable, vascular disease), BDM-E, rotigaptide, ETC-216,P-113, MBI-594AN, duramycin (inhaled, cystic fibrosis), SCV-07, OPI-45,Endostatin, Angiostatin, ABT-510, Bowman Birk Inhibitor Concentrate,XMP-629, 99 mTc-Hynic-Annexin V, kahalalide F, CTCE-9908, teverelix(extended release), ozarelix, rornidepsin, BAY-504798, interleukin4,PRX-321, Pepscan, iboctadekin, rhlactoferrin, TRU-015, IL-21, ATN-161,cilengitide, Albuferon, Biphasix, IRX-2, omega interferon, PCK-3145,CAP-232, pasireotide, huN901-DMI, ovarian cancer immunotherapeuticvaccine, SB-249553, Oncovax-CL, OncoVax-P, BLP-25, CerVax-16,multi-epitope peptide melanoma vaccine (MART-1, gp100, tyrosinase),nemifitide, rAAT (inhaled), rAAT (dermatological), CGRP (inhaled,asthma), pegsunercept, thymosinbeta4, plitidepsin, GTP-200, ramoplanin,GRASPA, OBI-1, AC-100, salmon calcitonin (oral, eligen), calcitonin(oral, osteoporosis), examorelin, capromorelin, Cardeva, velafermin,131I-TM-601, KK-220, T-10, ularitide, depelestat, hematide, Chrysalin(topical), rNAPc2, recombinant Factor V111 (PEGylated liposomal), bFGF,PEGylated recombinant staphylokinase variant, V-10153, SonoLysisProlyse, NeuroVax, CZEN-002, islet cell neogenesis therapy, rGLP-1,BIM-51077, LY-548806, exenatide (controlled release, Medisorb),AVE-0010, GA-GCB, avorelin, ACM-9604, linaclotid eacetate, CETi-1,Hemospan, VAL (injectable), fast-acting insulin (injectable, Viadel),intranasal insulin, insulin (inhaled), insulin (oral, eligen),recombinant methionyl human leptin, pitrakinra subcutancous injection,eczema), pitrakinra (inhaled dry powder, asthma), Multikine, RG-1068,MM-093, NBI-6024, AT-001, PI-0824, Org-39141, Cpn10 (autoimmunediseases/inflammation), talactoferrin (topical), rEV-131 (ophthalmic),rEV-131 (respiratory disease), oral recombinant human insulin(diabetes), RPI-78M, oprelvekin (oral), CYT-99007 CTLA4-Ig, DTY-001,valategrast, interferon alpha-n3 (topical), IRX-3, RDP-58, Tauferon,bile salt stimulated lipase, Merispase, alaline phosphatase, EP-2104R,Melanotan-II, bremelanotide, ATL-104, recombinant human microplasmin,AX-200, SEMAX, ACV-1, Xen-2174, CJC-1008, dynorphin A, SI-6603, LABGHRH, AER-002, BGC-728, malaria vaccine (virosomes, PeviPRO), ALTU-135,parvovirus B19 vaccine, influenza vaccine (recombinant neuraminidase),malaria/HBV vaccine, anthrax vaccine, Vacc-5q, Vacc-4x, HIV vaccine(oral), HPV vaccine, Tat Toxoid, YSPSL, CHS-13340, PTH(1-34) liposomalcream (Novasome), Ostabolin-C, PTH analog (topical, psoriasis),MBRI-93.02, MTB72F vaccine (tuberculosis), MVA-Ag85A vaccine(tuberculosis), FARA04, BA-210, recombinant plague FIV vaccine, AG-702,OxSODrol, rBetV1, Der-p1/Der-p2/Der-p7 allergen-targeting vaccine (dustmite allergy), PR1 peptide antigen (leukemia), mutant ras vaccine,HPV-16 E7 lipopeptide vaccine, labyrinthin vaccine (adenocarcinoma), CMLvaccine, WT1-peptide vaccine (cancer), IDD-5, CDX-110, Pentrys, Norelin,CytoFab, P-9808, VT-111, icrocaptide, telbermin (dermatological,diabetic foot ulcer), rupintrivir, reticulose, rGRF, HA,alpha-galactosidase A, ACE-011, ALTU-140, CGX-1160, angiotensintherapeutic vaccine, D-4F, ETC-642, APP-018, rhMBL, SCV-07 (oral,tuberculosis), DRF-7295, ABT-828, ErbB2-specific immunotoxin(anticancer), DT3SSIL-3, TST-10088, PRO-1762, Combotox,cholecystokinin-B/gastrin-receptor binding peptides, 111 ln-hEGF, AE-37,trasnizumab-DM1, Antagonist G, IL-12 (recombinant), PM-02734, IMP-321,rhIGF-BP3, BLX-883, CUV-1647 (topical), L-19 basedradioimmunotherapeutics (cancer), Re-188-P-2045, AMG-386, DC/1540/KLHvaccine (cancer), VX-001, AVE-9633, AC-9301, NY-ESO-1 vaccine(peptides), NA17.A2 peptides, melanoma vaccine (pulsed antigentherapeutic), prostate cancer vaccine, CBP-501, recombinant humanlactoferrin (dry eye), FX-06, AP-214, WAP-8294A (injectable), ACP-HIP,SUN-11031, peptide YY [3-36] (obesity, intranasal), FGLL, atacicept,BR3-Fc, BN-003, BA-058, human parathyroid hormone 1-34 (nasal,osteoporosis), F-18-CCR1, AT-1100 (celiac disease/diabetes), JPD-003,PTH(7-34) liposomal cream (Novasome), duramycin (ophthalmic, dry eye),CAB-2, CTCE-0214, GlycoPEGylated erythropoietin, EPO-Fc, CNTO-528,AMG-114, JR-013, Factor XIII, aminocandin, PN-951, 716155, SUN-E7001,TH-0318, BAY-73-7977, teverelix (immediate release), EP-51216, hGH(controlled release, Biosphere), OGP-I, sifuvirtide, TV4710, ALG-889,Org-41259, rhCC10, F-991, thymopentin (pulmonary diseases), r(m)CRP,hepatoselective insulin, subalin, L19-IL-2 fusion protein, elafin,NMK-150, ALTU-139, EN-122004, rhTPO, thrombopoietin receptor agonist(thrombocytopenic disorders), AL-108, AL-208, nerve growth factorantagonists (pain), SLV-317, CGX-1007, INNO-105, oral teriparatide(eligen), GEM-OSi, AC-162352, PRX-302, LFn-p24 fusion vaccine(Therapore), EP-1043, S pneumoniae pediatric vaccine, malaria vaccine,Neisseria meningitidis Group B vaccine, neonatal group B streptococcalvaccine, anthrax vaccine, HCV vaccine (gpE1+gpE2+MF-59), otitis mediatherapy, HCV vaccine (core antigen+ISCOMATRIX), hPTH(1-34) (transdermal,ViaDerm), 768974, SYN-101, PGN-0052, aviscumnine, BIM-23190,tuberculosis vaccine, multi-epitope tyrosinase peptide, cancer vaccine,enkastim, APC-8024, GI-5005, ACC-001, TTS-CD3, vascular-targeted TNF(solid tumors), desmopressin (buccal controlled-release), onercept, andTP-9201.

In some embodiments, the polypeptide is adalimumab (HUMIRA), infliximab(REMICADE™), rituximab (RITUXAN™/MAB THERA™) etanercept (ENBREL™),bevacizumab (AVASTIN™), trastuzumab (HERCEPTIN™), pegrilgrastim(NEULASTA™), or any other suitable polypeptide including biosimilars andbiobetters.

Other suitable polypeptides are those listed below and in Table 1 ofUS2016/0097074:

TABLE I Protein Product Reference Listed Drug interferon gamma-1bActimmune ® alteplase; tissue plasminogen activator Activase ®/Cathflo ®Recombinant antihemophilic factor Advate human albumin Albutein ®Laronidase Aldurazyme ® Interferon alfa-N3, human leukocyte derivedAlferon N ® human antihemophilic factor Alphanate ® virus-filtered humancoagulation factor IX AlphaNine ® SD Alefacept; recombinant, dimericfusion Amevive ® protein LFA3-Ig Bivalirudin Angiomax ® darbepoetin alfaAranesp ™ Bevacizumab Avastin ™ interferon beta-1a; recombinant Avonex ®coagulation factor IX BeneFix ™ Interferon beta-1b Betaseron ®Tositumomab BEXXAR ® antihemophilic factor Bioclate ™ human growthhormone BioTropin ™ botulinum toxin type A BOTOX ® Alemtuzumab Campath ®acritumomab; technetium-99 labeled CEA-Scan ® alglucerase; modified formof beta- Ceredase ® glucocerebrosidase imiglucerase; recombinant form ofbeta- Cerezyme ® glucocerebrosidase crotalidae polyvalent immune Fab,ovine CroFab ™ digoxin immune fab [ovine] DigiFab ™ Rasburicase Elitek ®Etanercept ENBREL ® epoietin alfa Epogen ® Cetuximab Erbitux ™algasidase beta Fabrazyme ® Urofollitropin Fertinex ™ follitropin betaFollistim ™ Teriparatide FORTEO ® human somatropin GenoTropin ® GlucagonGlucaGen ® follitropin alfa Gonal-F ® antihemophilic factor Helixate ®Antihemophilic Factor; Factor XIII HEMOFIL adefovir dipivoxil Hepsera ™Trastuzumab Herceptin ® Insulin Humalog ® antihemophilic factor/vonWillebrand factor Humate-P ® complex-human Somatotropin Humatrope ®Adalimumab HUMIRA ™ human insulin Humulin ® recombinant humanhyaluronidase Hylenex ™ interferon alfacon-1 Infergen ® eptifibatideIntegrilin ™ alpha-interferon Intron A ® Palifermin Kepivance AnakinraKineret ™ antihemophilic factor Kogenate ® FS insulin glargine Lantus ®granulocyte macrophage colony-stimulating Leukine ®/Leukine ® factorLiquid lutropin alfa for injection Luveris OspA lipoprotein LYMErix ™Ranibizumab LUCENTIS ® gemtuzumab ozogamicin Mylotarg ™ GalsulfaseNaglazyme ™ Nesiritide Natrecor ® Pegfilgrastim Neulasta ™ OprelvekinNeumega ® Filgrastim Neupogen ® Fanolesomab NeutroSpec ™ (formerlyLeuTech ®) somatropin [rDNA] Norditropin ®/Norditropin Nordiflex ®Mitoxantrone Novantrone ® insulin; zinc suspension; Novolin L ® insulin;isophane suspension Novolin N ® insulin, regular; Novolin R ® InsulinNovolin ® coagulation factor VIIa NovoSeven ® Somatropin Nutropin ®immunoglobulin intravenous Octagam ® PEG-L-asparaginase Oncaspar ®abatacept, fully human soluable fusion Orencia ™ protein muromomab-CD3Orthoclone OKT3 ® high-molecular weight hyaluronan Orthovisc ® humanchorionic gonadotropin Ovidrel ® live attenuated BacillusCalmette-Guerin Pacis ® peginterferon alfa-2a Pegasys ® pegylatedversion of interferon alfa-2b PEG-Intron ™ Abarelix (injectablesuspension); Plenaxis ™ gonadotropin-releasing hormone antagonistepoietin alfa Procrit ® Aldesleukin Proleukin, IL-2 ® SomatremProtropin ® dornase alfa Pulmozyme ® Efalizumab; selective, reversibleT-cell RAPTIVA ™ blocker combination of ribavirin and alpha interferonRebetron ™ Interferon beta 1a Rebif ® antihemophilic factorRecombinate ® rAHF/ antihemophilic factor ReFacto ® Lepirudin Refludan ®Infliximab REMICADE ® Abciximab ReoPro ™ Reteplase Retavase ™ RituximaRituxan ™ interferon alfa-2^(a) Roferon-A ® Somatropin Saizen ®synthetic porcine secretin SecreFlo ™ Basiliximab Simulect ® EculizumabSOLIRIS (R) Pegvisomant SOMAVERT ® Palivizumab; recombinantly produced,Synagis ™ humanized mAb thyrotropin alfa Thyrogen ® TenecteplaseTNKase ™ Natalizumab TYSABRI ® human immune globulin intravenous 5%Venoglobulin-S ® and 10% solutions interferon alfa-n1, lymphoblastoidWellferon ® drotrecogin alfa Xigris ™ Omalizumab; recombinantDNA-derived Xolair ® humanized monoclonal antibody targetingimmunoglobulin-E Daclizumab Zenapax ® ibritumomab tiuxetan Zevalin ™Somatotropin Zorbtive ™ (Serostim ®)

In embodiments, the polypeptide is a hormone, blood clotting/coagulationfactor, cytokine/growth factor, antibody molecule, fusion protein,protein vaccine, or peptide as shown in Table 2.

TABLE 2 Exemplary Products Therapeutic Product type Product Trade NameHormone Erythropoietin, Epoein-α Epogen, Procrit Darbepoetin-α AranespGrowth hormone (GH), Genotropin, Humatrope, Norditropin, somatotropinNovIVitropin, Nutropin, Omnitrope, Protropin, Siazen, Serostim,Valtropin Human follicle- Gonal-F, Follistim stimulating hormone (FSH)Ovidrel Human chorionic Luveris gonadotropin GlcaGen Lutropin-α GerefGlucagon ChiRhoStim (human peptide), Growth hormone releasing SecreFlo(porcine peptide) hormone (GHRH) Thyrogen Secretin Thyroid stimulatinghormone (TSH), thyrotropin Blood Factor VIIa NovoSevenClotting/Coagulation Factor VIII Bioclate, Helixate, Kogenate, FactorsRecombinate, ReFacto Factor IX Antithrombin III (AT-III) Benefix ProteinC concentrate Thrombate III Ceprotin Cytokine/Growth Type Ialpha-interferon Infergen factor Interferon-αn3 (IFNαn3) Alferon NInterferon-β1a (rIFN- β) Avonex, Rebif Interferon-β1b (rIFN- β)Betaseron Interferon-γ1b (IFN γ) Actimmune Aldesleukin (interleukinProleukin 2(IL2), epidermal theymocyte activating factor; ETAF KepivancePalifermin (keratinocyte Regranex growth factor; KGF) Becaplemin(platelet- Anril, Kineret derived growth factor; PDGF) Anakinra(recombinant IL1 antagonist) Antibody molecules Bevacizumab (VEGFAAvastin mAh) Erbitux Cetuximab (EGFR mAb) Vectibix Panitumumab (EGFRCampath mAb) Rituxan Alemtuzumab (CD52 Herceptin mAb) Orencia Rituximab(CD20 Humira chimeric Ab) Enbrel Trastuzumab (HER2/Neu mAb) RemicadeAbatacept (CTLA Ab/Fc Amevive fusion) Raptiva Adalimumab Tysabri (TNFαmAb) Soliris Etanercept (TNF Orthoclone, OKT3 receptor/Fc fusion)Infliximab (TNFα chimeric mAb) Alefacept (CD2 fusion protein) Efalizumab(CD11a mAb) Natalizumab (integrin α4 subunit mAb) Eculizumab (C5mAb)Muromonab-CD3 Other: Insulin Humulin, Novolin Fusion Hepatitis B surfaceEngerix, Recombivax HB proteins/Protein antigen (HBsAg)vaccines/Peptides HPV vaccine Gardasil OspA LYMErix Anti-Rhesus(Rh)Rhophylac immunoglobulin G Fuzeon Enfuvirtide Spider silk, e.g., fibrionQMONOS

In embodiments, the protein is multispecific protein, e.g., a bispecificantibody as shown in Table 3.

TABLE 3 Bispecific Formats Name (other names, Proposed Diseases (orsponsoring BsAb mechanisms of Development healthy organizations) formatTargets action stages volunteers) Catumaxomab BsIgG: CD3, Retargeting ofT Approved in Malignant (Removab ®, Triomab EpCAM cells to tumor, EUascites in Fresenius Fc mediated EpCAM Biotech, Trion effector positivetumors Pharma, functions Neopharm) Ertumaxomab BsIgG: CD3, HER2Retargeting of T Phase I/II Advanced solid (Neovii Biotech, Triomabcells to tumor tumors Fresenius Biotech) Blinatumomab BiTE CD3, CD19Retargeting of T Approved in Precursor B-cell (Blincyto ®, AMG cells totumor USA ALL 103, MT 103, Phase II and ALL MEDI 538, III DLBCL Amgen)Phase II NHL Phase I REGN1979 BsAb CD3, CD20 (Regeneron) Solitomab (AMGBiTE CD3, Retargeting of T Phase I Solid tumors 110, MT110, EpCAM cellsto tumor Amgen) MEDI 565 BiTE CD3, CEA Retargeting of T Phase IGastrointestinal (AMG 211, cells to tumor adenocancinoma MedImmune,Amgen) RO6958688 BsAb CD3, CEA (Roche) BAY2010112 BiTE CD3, PSMARetargeting of T Phase I Prostate cancer (AMG 212, cells to tumor Bayer;Amgen) MGD006 DART CD3, CD123 Retargeting of T Phase I AML (Macrogenics)cells to tumor MGD007 DART CD3, gpA33 Retargeting of T Phase IColorectal (Macrogenics) cells to tumor cancer MGD011 DART CD19, CD3(Macrogenics) SCORPION BsAb CD3, CD19 Retargeting of T (Emergent cellsto tumor Biosolutions, Trubion) AFM11 (Affimed TandAb CD3, CD19Retargeting of T Phase I NHL and ALL Therapeutics) cells to tumor AFM12(Affimed TandAb CD19, CD16 Retargeting of Therapeutics) NK cells totumor cells AFM13 (Affimed TandAb CD30, Retargeting of Phase IIHodgkin's Therapeutics) CD16A NK cells to Lymphoma tumor cells GD2(Barbara T cells CD3, GD2 Retargeting of T Phase I/II Neuroblastoma AnnKarmanos preloaded cells to tumor and Cancer Institute) with BsAbosteosarcoma pGD2 (Barbara T cells CD3, Her2 Retargeting of T Phase IIMetastatic Ann Karmanos preloaded cells to tumor breast cancer CancerInstitute) with BsAb EGFRBi-armed T cells CD3, EGFR Autologous Phase ILung and other autologous preloaded activated T cells solid tumorsactivated T cells with BsAb to EGFR- (Roger Williams positive tumorMedical Center) Anti-EGFR- T cells CD3, EGFR Autologous Phase I Colonand armed activated preloaded activated T cells pancreatic T-cells(Barbara with BsAb to EGFR- cancers Ann Karmanos positive tumor CancerInstitute) rM28 (University Tandem CD28, Retargeting of T Phase IIMetastatic Hospital scFv MAPG cells to tumor melanoma Tübingen) IMCgp100ImmTAC CD3, Retargeting of T Phase I/II Metastatic (Immunocore) peptidecells to tumor melanoma MHC DT2219ARL 2 scFv CD19, CD22 Targeting ofPhase I B cell leukemia (NCI, University linked to protein toxin to orlymphoma of Minnesota) diphtheria tumor toxin XmAb5871 BsAb CD19,(Xencor) CD32b NI-1701 BsAb CD47, CD19 (NovImmune) MM-111 BsAb ErbB2,(Merrimack) ErbB3 MM-141 BsAb IGF-1R, (Merrimack) ErbB3 NA (Merus) BsAbHER2, HER3 NA (Merus) BsAb CD3, CLEC12A NA (Merus) BsAb EGFR, HER3 NA(Merus) BsAb PD1, undisclosed NA (Merus) BsAb CD3, undisclosedDuligotuzumab DAF EGFR, Blockade of 2 Phase I and II Head and neck(MEHD7945A, HER3 receptors, Phase II cancer Genentech, ADCC ColorectalRoche) cancer LY3164530 (Eli Not EGFR, MET Blockade of 2 Phase IAdvanced or Lily) disclosed receptors metastatic cancer MM-111 HSA bodyHER2, Blockade of 2 Phase II Gastric and (Merrimack HER3 receptors PhaseI esophageal Pharmaceuticals) cancers Breast cancer MM-141, IgG-scFvIGF-1R, Blockade of 2 Phase I Advanced solid (Merrimack HER3 receptorstumors Pharmaceuticals) RG7221 CrossMab Ang2, Blockade of 2 Phase ISolid tumors (RO5520985, VEGF A proangiogenics Roche) RG7716 (Roche)CrossMab Ang2, Blockade of 2 Phase I Wet AMD VEGF A proangiogenicsOMP-305B83 BsAb DLL4/VEGF (OncoMed) TF2 Dock and CEA, HSG PretargetingPhase II Colorectal, (Immunomedics) lock tumor for PET breast and lungor radioimaging cancers ABT-981 DVD-Ig IL-1α, IL-Iβ Blockade of 2 PhaseII Osteoarthritis (AbbVie) proinflammatory cytokines ABT-122 DVD-Ig TNF,IL- Blockade of 2 Phase II Rheumatoid (AbbVie) 17A proinflammatoryarthritis cytokines COVA322 IgG- TNF, IL17A Blockade of 2 Phase I/IIPlaque psoriasis fynomer proinflammatory cytokines SAR156597 TetravalentIL-13, IL-4 Blockade of 2 Phase I Idiopathic (Sanofi) bispecificproinflammatory pulmonary tandem IgG cytokines fibrosis GSK2434735 Dual-IL-13, IL-4 Blockade of 2 Phase I (Healthy (GSK) targetingproinflammatory volunteers) domain cytokines Ozoralizumab Nanobody TNF,HSA Blockade of Phase II Rheumatoid (ATN103, proinflammatory arthritisAblynx) cytokine, binds to HSA to increase half-life ALX-0761 NanobodyIL-17A/F, Blockade of 2 Phase I (Healthy (Merck Serono, HSAproinflammatory volunteers) Ablynx) cytokines, binds to HSA to increasehalf-life ALX-0061 Nanobody IL-6R, HSA Blockade of Phase I/II Rheumatoid(AbbVie, proinflammatory arthritis Ablynx; cytokine, binds to HSA toincrease half-life ALX-0141 Nanobody RANKL, Blockade of Phase IPostmenopausal (Ablynx, HSA bone resorption, bone loss Eddingpharm)binds to HSA to increase half-life RG6013/ACE910 ART-Ig Factor IXa,Plasma Phase II Hemophilia (Chugai, Roche) factor X coagulation

These and other modifications and variations to the present inventionmay be practiced by those of ordinary skill in the art, withoutdeparting from the spirit and scope of the present invention, which ismore particularly set forth in the appended claims. In addition, itshould be understood that aspects of the various embodiments may beinterchanged both in whole or in part. Furthermore, those of ordinaryskill in the art will appreciate that the foregoing description is byway of example only, and is not intended to limit the invention sofurther described in such appended claims.

What is claimed:
 1. A process for propagating a cell culture comprising:exposing a cell culture in a bioreactor to a coherent light sourcecausing light to scatter; measuring an intensity of the scattered lightusing Raman spectroscopy; determining a concentration of a parameter inthe cell culture using a controller based on the measured scatteredlight intensity and, based on the determined concentration of theparameter, the controller selectively increasing or decreasing flow of aparameter influencing substance to the bioreactor in order to maintainthe parameter within preset limits; and wherein the cell culture isexposed to the coherent light source for determining the concentrationof the parameter at least once every 4 hours; and wherein theconcentration of the parameter is determined over the first 1 to 6 daysprior to the controller selectively increasing or decreasing the flow ofthe parameter influencing substance to the bioreactor.
 2. A process asdefined in claim 1, wherein the concentration of the parameter isdetermined by comparing light intensity data to reference data.
 3. Aprocess as defined in claim 1, wherein the controller includes apredictive model that extrapolates a future concentration of theparameter based on the determined concentration of the parameter andselectively increases or decreases the flow of the parameter influencingsubstance to the bioreactor in order to maintain the parameter withinpreset limits based on the calculated future concentration.
 4. A processas defined in claim 3, wherein the concentration of the parameter isdetermined for from about 12 hours to about 4 days prior to thecontroller extrapolating a future concentration of the parameter.
 5. Aprocess as defined in claim 3, wherein the predictive model is createdby breaking correlations in at least one parameter during referencetesting.
 6. A process as defined in claim 1, wherein the parametercomprises glucose, lactate, glutamate, ammonium, viable cellconcentration, total cell concentration, or product concentration.
 7. Aprocess as defined in claim 1, wherein the concentration of at least twodifferent parameters are determined in the cell culture by thecontroller based upon the measured scattered light intensity and whereinthe controller selectively increases or decreases flow of one or moreparameter influencing substances to the bioreactor in order to maintainthe at least two parameters within preset limits.
 8. A process asdefined in claim 7, wherein the at least two different parameterscomprise two parameters selected from the group including glucose,lactate, glutamate, ammonium, viable cell concentration, total cellconcentration, or product concentration.
 9. A process as defined inclaim 1, wherein the parameter influencing substance comprises anutrient media.
 10. A process as defined in claim 1, wherein the cellculture contains mammalian cells.
 11. A process as defined in claim 1,wherein the cell culture contains stem cells, T cells or immune cells.12. A process as defined in claim 1, further comprising the step ofconducting statistical analysis of the measured scattered lightintensity, the statistical analysis comprising applying a standardnormal variate, applying a first derivative, and selecting a spectralrange that correlates with the parameter, and further comprising thestep of detrending after applying the standard normal variate.
 13. Aprocess as defined in claim 1, wherein the cell culture is propagated ina batch process for from about 2 days to about 28 days and thenharvested.
 14. A process as defined in claim 1, wherein the controlleris programed with a control model for selectively increasing ordecreasing flow of the parameter influencing substance, the controlmodule being capable of maintaining the parameter within preset limitswithin different cell cultures containing different types of cells. 15.A process as defined in claim 1, wherein the concentration of theparameter is determined by the controller over the first 2 to 4 daysprior to the controller selectively increasing or decreasing the flow ofthe parameter influencing substance to the bioreactor.
 16. A system forpropagating a cell culture comprising: a bioreactor defining a hollowinterior for receiving a cell culture, the bioreactor including aplurality of ports for feeding and/or removing materials from the hollowmaterial; a nutrient media feed for feeding a nutrient media to thehollow interior of the bioreactor, the nutrient media feed being influid communication with at least one of the ports on the bioreactor; alight conveying device in communication with the hollow interior of thebioreactor, the light conveying device for conveying light to thebioreactor and away from the bioreactor; a coherent light source incommunication with the light conveying device for exposing a cellculture in the bioreactor to a beam of light; a Raman spectrometer incommunication with the light conveying device for receiving scatterlight from the hollow interior of the bioreactor after a cell culture inthe bioreactor has been exposed to a beam of light by the coherent lightsource, the Raman spectrometer being configured to measure an intensityof the scattered light; and a controller in communication with the Ramanspectrometer and the nutrient media feed, the controller beingconfigured to determine a concentration of a parameter of a cell culturecontained in the hollow interior of the bioreactor based on lightintensity data received from the Raman spectrometer, the controller,based on the determined concentration of the parameter, also beingconfigured to control the nutrient media feed for selectively increasingor decreasing flow of a nutrient media into the bioreactor in order tomaintain the parameter within preset limits, wherein the controllerincludes a predictive model that extrapolates a future concentration ofthe parameter based on the determined concentration of the parameter andcontrols the nutrient media feed for selectively increasing ordecreasing a flow of a nutrient media to the bioreactor to maintain theparameter within preset limits based on the calculated futureconcentration and wherein the controller is programmed to determine theconcentration of the parameter for over 1 day to 6 days in order tobuild predictive data prior to selectively increasing or decreasing theflow of the nutrient media feed to the bioreactor.